使用此数据框，dff:
A B
0 0 a
1 1 a
2 2 b
3 3 b
4 4 b
5 5 b
6 6 c
7 7 c
我了解如何
len(dff) == 8但是，我不明白以下答案:
dff['counts'] = np.arange(len(dff))
这是
A B counts
0 0 a 0
1 1 a 1
2 2 b 2
3 3 b 3
4 4 b 4
5 5 b 5
6 6 c 6
7 7 c 7
dff['counts'] 每行不应该是 8 吗？引擎盖下发生了什么？ 最佳答案
您似乎误解了 np.arange 的作用:
In [32]:
np.arange(8)
Out[32]:
array([0, 1, 2, 3, 4, 5, 6, 7])
这里 df 的长度用于设置
stop 参数:从文档:
numpy.arange([start, ]stop, [step, ]dtype=None)
Return evenly spaced values within a given interval.
Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop). For integer arguments the function is equivalent to the Python built-in range function, but returns an ndarray rather than a list.
When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use linspace for these cases.
Parameters:
start : number, optional
Start of interval. The interval includes this value. The default start value is 0.
stop : number
End of interval. The interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out.
step : number, optional
Spacing between values. For any output out, this is the distance between two adjacent values, out[i+1] - out[i]. The default step size is 1. If step is specified, start must also be given.
dtype : dtype
The type of the output array. If dtype is not given, infer the data type from the other input arguments.
Returns:
arange : ndarray
Array of evenly spaced values.
For floating point arguments, the length of the result is ceil((stop - start)/step). Because of floating point overflow, this rule may result in the last element of out being greater than stop.
如果您想将每一行设置为相同的值，您可以这样做
In [34]:
dff['counts'] = len(dff)
dff
Out[34]:
A B counts
0 0 a 8
1 1 a 8
2 2 b 8
3 3 b 8
4 4 b 8
5 5 b 8
6 6 c 8
7 7 c 8
关于python - len() 与 arange() 的行为，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/32190009/